Genetic Mixture Analysis Supports Recalibration of the Fishery Regulation Assessment Model
Bibliographic record
Abstract
Abstract Management of the commercially important Washington coastal Chinook Salmon Oncorhynchus tshawytscha troll fishery depends on the Chinook Salmon Fishery Regulation Assessment Model (FRAM). The Chinook Salmon FRAM uses historical and contemporary coded wire tag recoveries to estimate abundance and exploitation rates for particular indicator stocks. Those estimates are used to set limits on overall harvest and protect sensitive stocks. Current efforts are underway to implement a newer “base period” (time period on which exploitation rates are based). Our collaboration of science, management, and industry used genetic mixture modeling to provide independent stock composition estimates supporting FRAM recalibration. Genetic modeling suggested that total catch includes a much smaller proportion of a limiting Columbia River stock, a larger fraction of Canadian stocks, and an abundant Oregon coastal stock not previously included in the FRAM. Our results focus attention on particular stocks that will benefit from refinements in the Chinook Salmon FRAM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".